A Multi-swarm Co-evolutionparticle Swarm Optimization Algorithm Combined with the Extremal Optimization Algorithm

GE Peng; LIU Hong-yi

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A Multi-swarm Co-evolutionparticle Swarm Optimization Algorithm Combined with the Extremal Optimization Algorithm

更新时间：2026-09-04

- A Multi-swarm Co-evolutionparticle Swarm Optimization Algorithm Combined with the Extremal Optimization Algorithm
- Vol. 31, Issue 6, Pages: 176-179(2014)
- 作者机构：
  
  1. 1 海军装备部驻西安军事代表局,陕西,西安,710054
  2. 2 解放军边防学院,陕西,西安,710108
- 作者简介：
- 基金信息：
- DOI：
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- Published：2014
- 稿件说明：
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葛澎, 刘宏义. 结合极值优化的多粒子群协同进化算法[J]. 微电子学与计算机, 2014, 31(6): 176-179.

GE Peng, LIU Hong-yi. A Multi-swarm Co-evolutionparticle Swarm Optimization Algorithm Combined with the Extremal Optimization Algorithm[J]. 2014, 31(6): 176-179.
葛澎, 刘宏义. 结合极值优化的多粒子群协同进化算法[J]. 微电子学与计算机, 2014, 31(6): 176-179. DOI：

GE Peng, LIU Hong-yi. A Multi-swarm Co-evolutionparticle Swarm Optimization Algorithm Combined with the Extremal Optimization Algorithm[J]. 2014, 31(6): 176-179. DOI：

摘要

多粒子群协同进化算法是一种群智能算法，具有智能性、通用性、并行性和全局搜索能力，能够很好地解决全局寻优问题，但其保持粒子多样性的机制和协同进化的机制有待做进一步的改进.为了进一步提高多粒子群协同进化算法的寻优效率，提出了一种结合极值优化的多粒子群协同进化算法，它将多粒子群协同进化算法的全局搜索能力与极值优化算法的局部搜索能力进行了结合.最后通过实验验证了该算法的有效性.

Abstract

The multi-swarm co-evolution particle swarm optimization algorithm is an algorithm based on swarm intelligence

with intelligence

versatility

parallelism and global searching ability

can solve the problem of global optimization very well

however

mechanism to keep the diversity of particles and co-evolution mechanism need to be further improved. In order to further improve the optimization efficiency of the muti-swarm co-evolution particle swarm optimization algorithm

we propose a new muti-swarm co-evolution particle swarm optimization algorithm combined with the extreme optimization algorithm(MCPSOEO)

combine the global searching ability of the multi-swarm co-evolution particle swarm optimization algorithm and the local searching ability of the extreme optimization.Finally

the experimental results prove the effectiveness of the algorithm.

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